The present subject matter provides an audio system including two or more acoustic sensors, a beamformer, and a noise reduction post-filter to optimize the performance of noise reduction algorithms used to capture an audio source.
Many mobile devices and other speakerphone/handsfree communication systems, including smartphones, tablets, hand free car kits, etc., include two or more microphones or other acoustic sensors for capturing sounds for use in various applications. For example, such systems are used in speakerphones, video VOIP, voice recognition applications, audio/video recording, etc. The overall signal-to-noise ratio of the multi-microphone signals is typically improved using beamforming algorithms for noise cancellation. Generally speaking, beamformers use weighting and time-delay algorithms to combine the signals from the various microphones into a single signal. Beamformers can be fixed or adaptive algorithms. An adaptive post-filter is typically applied to the combined signal after beamforming to further improve noise suppression and audio quality of the captured signal. The post-filter is often analogous to regular mono microphone noise suppression (i.e., uses Wiener Filtering or Spectral Subtraction), but it has the advantage over the mono microphone case in that the multi microphone post-filter can also use spatial information about the sound field for enhanced noise suppression.
For far-field situations, such as speakerphone/hands-free applications in which both the target source (e.g., the user's voice) and the noise sources are located farther away from the microphones, it is common for the multi-microphone post-filter to use some variant of the so-called Zelinski post-filter. This technique derives Wiener gains using the ratio of multi-microphone cross-spectral densities to auto-spectral densities, and involves the following assumptions:                1. The target signal (e.g., the voice) and noise are uncorrelated;        2. The noise power spectrum is approximately equal at all microphones; and        3. The noise is uncorrelated between microphone signals.        
Unfortunately, in real-world situations, the third assumption is not valid at low frequencies, and, if the noise source is directional, is not valid at any frequency. In addition, depending on diffraction effects due to the device's form factor, room acoustics, microphone mismatch, etc., the second assumption may not be valid at some frequencies. Therefore, the use of a Zelinski post-filter is not an ideal solution for noise reduction for multi-microphone mobile devices in real-world conditions.
Accordingly, there is a need for an efficient and effective system and method for improving the noise reduction performance of multi-microphone systems employed in mobile devices that does not rely on assumptions about inter-microphone correlation and noise power levels, as described and claimed herein.